基于多码本lvq的聚类人工神经网络

M. Anwar Ma'sum, H. Sanabila, W. Jatmiko, Aprinaldi
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引用次数: 6

摘要

本文采用聚类方法提出了基于多码本lvq的人工神经网络分类器。分类器是LVQ、LVQ2-1、GLVQ和FNGLVQ。构建多码本的聚类算法有K-Means、IK-Means和GMM。实验结果表明,在多码本的合成数据集上,采用GMM聚类的FNGLVQ比FNGLVQ有最高的改进,提高了19.53%。而在基准数据集上,使用K-Means聚类的多码本LVQ2-1与LVQ-2.1相比,具有最高的改进,提高了5.83%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multi codebook LVQ-based artificial neural network using clustering approach
In this paper we proposed multicodebook LVQ-based artificial neural network classifier using clustering approach. The classifiers are LVQ, LVQ2-1, GLVQ, and FNGLVQ. The clustering algorithm used to build multi codebook is K-Means, IK-Means, and GMM. Experiment result shows that on synthteic dataset multi codebook FNGLVQ using GMM clustering has higest improvement with 19,53% mprovement compared to FNGLVQ. Whereas on bencmark dataset multi codebook LVQ2-1 using K-Means clustering has higest improvement with 5,83% improvement cmpared to LVQ-2.1.
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